Travelling to new places always requires significant preparations if one wants to discover some tourist attractions or just look where to spend some spare time. For larger cities it is less of an issue since numerous traveling guides list and describe popular points of interest (POI). However, the problem arises for smaller cities with fewer popular attractions. In this case, either no relevant sources of information exist or the sources represent very subjective opinions of only a small group of a population. Therefore, user-generated data may lead to less subjective recommendations as it incorporates interests of a vast pool of users.
It is known in the art to use user-generated data for discovering and recommending different POI for tourists. However, recommending areas instead of points of interest match the traditional tourist goals and allows to robustly incorporating the interests of many users resulting in less subjective recommendations. Recommending areas of interest (AOI) may answer the following question: “Where can I spend T minutes/hours walking around to observe as many attractive places as possible?”
Various POI discovery methods are presently being applied to AOI discovery. Given a collection of geotagged photos, the task is to find POIs. Needless to say, some areas are may be considered attractive if there are many photos taken in/around the area. K-means is a well-known mean-based clustering technique that was proposed for POI discovery but can be adapted for AOI discovery. K-means partitions the whole set of photos into closely connected subsets. The center of mass of every cell can represent a specific POI, while a cell itself can represent AOI. The major disadvantage of this method is that it assigns every single point to one cluster, which may result in large cells with low photo density. Mean Shift instead looks only for areas with high photo density. Similarly to the previous technique, it starts with some coordinates and iteratively shifts them in the direction of larger density values. This results in local density peaks which may be considered as POI candidates. However, the method does not allow identifying an AOI around or in proximity to the local peak.
The most common method applied for POI and AOI discovery is DBSCAN. The method adds a new photo to a cluster, if it is within a certain radius around the already clustered photos and if it has at least a minimum number of neighboring photos. The radius and the number of neighboring photos are parameters of this clustering technique. Photos in low density regions are not clustered resulting in less interesting areas. However, the main disadvantage of the method is that for any set of parameters, the discovered regions greatly vary in size depending on their photo density. Recommending such regions to a tourist does not make much sense since the walking time spent within the regions greatly increases. A recent modification to the previous technique, called P-DBSCAN, aims at solving this problem by introducing a third parameter: maximum possible density change when adding a new point. Although it partially overcomes some disadvantages of DBSCAN, this technique requires testing for tuning the various parameters that are involved.